A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.

1. Responsible Conduct of Research:
Managing Data
Tobin Magle
Data Management
Specialist
Nicole Kaplan
Information
Manager
Daniel Draper
Digital Repositories
Unit Coordinator

2. Responsible Conduct of Research:
The data management firehose!
C. Tobin Magle, PhD
Please ask me for help with data management!

3. My Background: molecular microbiology
(1) Magle CT et al Infect Immun. 2014 Feb;82(2):618-25. doi: 10.1128/IAI.00444-13. Epub 2013 Nov 25.
(2) Sun W, Tanaka TQ, Magle CT, et al.. Sci Rep. 2014 Jan 17;4:3743. doi: 10.1038/srep03743.

4. Data Workshops

5. Individual help for ANY data topic
How do I
write a
DMP?
How do I
organize my
data?
How do I
clean and
format my
data?
How do
I use R?
How do I get
my data ready
to share?
How do I comply
with funder
mandates?
What DM tools
are there for
collaboration?
How do
I use R?

6. Data Management Services
https://lib.colostate.edu/services/data-management

7. What is data
management?
The policies, practices and procedures needed to
manage the storage, access and preservation of
data produced from a research project

8. data management != data sharing
• but the same principles apply to both

9. *ok not everything, but most things

10. More researchers
https://www.nsf.gov/statistics/2016/nsf16300/digest/nsf16300.pdf

11. See arXiv:1402.4578 for details

12. Working
Email
Data are extant
(If status known)
Status of data
(if response)
Response
(if email
working)
doi:10.1016/j.cub.2013.11.014

13. We are losing vast amounts of data
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Who is responsible?

14. CSU data policy
General points:
• The university owns, and is therefore ultimately responsible for
research data
• Researchers are the data managers
• The university promotes openness
http://policylibrary.colostate.edu/policy.aspx?id=737

15. You’re a Data Manager
http://www.phdcomics.com/comics/archive.php?comicid=382

16. CSU data policy
Research Data Associated with Theses and Dissertations
To preserve the complete scholarly record of the author, data
sets must be incorporated. Therefore, a student depositing their
thesis or dissertation is required to make discoverable,
accessible and available their associated data sets in
accordance with this policy and provisions of the University’s
Digital Repository. Access and rights management (embargo
period, access limited to specific IP addresses) shall be the same
for the associated data sets as it is for the thesis or the
dissertation.
http://policylibrary.colostate.edu/policy.aspx?id=737

17. When should data
management happen?
Throughout the whole research cycle

18. Hypothesis
The research cycle

19. Hypothesis
Experimental
design
The research cycle

20. Hypothesis Data
Experimental
design
The research cycle

21. Hypothesis Data
Experimental
design
Results
The research cycle

22. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

23. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

24. Hypothesis Data
Experimental
design
ResultsArticle
Data
Management
Plans
The research cycle

25. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Analysis
The research cycle

26. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
The research cycle

27. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
The research cycle

28. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Reuse
The research cycle

29. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Reuse
The research cycle

30. What is research data?
• “The recorded factual material commonly accepted
in the scientific community as necessary to validate
research findings”
- White House Office of Management and Budget
• Reality: Applies to any research product

31. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Reuse
Working data vs. archived data Working
Archived

32. What is a data
management plan?
A description of how you plan to describe, preserve
and share your research data.
Often required by funding agencies

33. Successful DMPs include
• A data inventory, including type(s) and size
• A strategy for describing the data
• A plan for preserving the data
• A method for access to the data
Always make sure to follow funder requirements

34. Tool: DMPTool
• Review requirements from
different agencies
• https://dmptool.org/guidance
• Create new DMPs based on
funding agency templates
• Search public DMPs

35. Data inventory
• What type of data are you going to collect?
• What file type will be produced?
• What size will these files be? How many files?
• How will you organize the data?
• What other research outputs will be produced?
• Code/Software?
• Templates/protocols?

36. Data inventory
• What type of data are you going to collect?
• What file type will be produced?
• What size will these files be? How many files?
• What other research outputs will be produced?
• Code/Software?
• Templates/protocols?
miRNA sequences
FASTQ files
1 GB per file
x 64 strains
x 3 replicates
-------------------
~200 GB
R scripts for
analysis and
visualization
Data use tutorials

37. Data formats
• Avoid proprietary formats
• Know what software can read your data
Proprietary Format Open Format
Excel (.xls, .xlsx) Comma Separated Values (.csv)
Word (.doc, .docx) plain text (.txt)
PowerPoint (.ppt, .pptx) PDF/A (.pdf)
Photoshop (.psd) TIFF (.tif, .tiff)
Quicktime (.mov) MPEG-4 (.mp4)
MPEG 4 Protected audio (.m4p) MP3 (.mp3)

38. Q’s: Data Inventory
What kind of data are you going to collect?
What file type will be produced?
What size will these files be? How many files?
What other research outputs will be produced?

39. Folder systems
• Identify ways to divide your
data into categories
(Attributes)
• Top level organization is the
most important attribute
• Provide documentation

40. Hierarchical Organization
my_thesis
chapter1 chapter2 chapter3 chapter4
raw_data
replicate1
replicate2
processed_data code
Processing
cleaning
results
tables
figures

41. Q’s: Data Organization
• What kinds of files are there? (See data inventory)
• How could you group them?
• Project?
• Time?
• Location?
• File type?
• What are the most important attributes?

42. Tool: Open Science Framework
• Components
• Add-ons
• Contributors
• Wiki
http://help.osf.io/m/collaborating/l/524109-using-the-wiki http://www.slideshare.net/DuraSpace/121014-
slides-roadmap-to-the-future-of-share

43. Organization rules
• Be consistent
• One directory per project
• Separate subdirectories for
• Raw data
• Processed data
• Code (processing and analysis)
• Output
• Make raw data read-only
• Make README files
http://help.osf.io/m/60347/l/611391-organizing-files

44. Example: Temperature data

45. A strategy for describing the data
• Metadata: Relevant information
for re-creation and re-use
• Contact info
• How data was collected
• Details about collection
• Date, location of collection
• Units
• Can be as simple as a text file

46. Metadata standards
• Dublin Core: http://dublincore.org/documents/dcmi-terms/
• Can be applied to anything
• Many discipline specific metadata standards
• EML: https://knb.ecoinformatics.org/#external//emlparser/docs/index.html
• MIAME: http://fged.org/projects/miame/
• Search for other standards:
• http://www.dcc.ac.uk/resources/metadata-standards
• https://biosharing.org/standards/

47. Genomics example (NCBI template)

48. Q’s: Describe your data
What do people need to know to reuse your data?
Are there any discipline-specific metadata standards?
What format will you describe your data in (text, XML, tabular)?
What fields will you include (author, date, format, identifier?)

49. A plan for preserving the data
• Where will it be stored?
- Backups
• Necessary metadata and
other products
• Who is responsible?
• How long?

50. Ellin, A. Rutgers Student Offer $1,000 for Data on Stolen Laptop.abcNEWS via Good Morning America. April 26, 2013.
http://abcnews.go.com/blogs/business/2013/04/rutgers-student-offers-1000-for-data-on-stolen-laptop/
Backup

51. Back up recommendations
• Store in geographically
distinct locations
• How often?
• Automation: Will you remember
to do it manually?
• Security: Are you working with
PHI?

52. Q’s: Preservation plan
What will you store?
Who will be responsible for the data (person or position)?
How long will you store it?
Where will you store it?
How will you back it up?
*Differentiate between working vs. archived

53. A method to access the data
• Important to funding agencies
• Reproduce existing research
• Promote further research
• Must be easily available:
• No “by request only”
• Embargoes are “ok”
• Data security: consider privacy
and IP issues before sharing

54. Data access and sharing best practices
• Non-proprietary formats
• Include metadata
• As open as possible
• Follow CSU research data policy

55. Trusted Repositories: store and share
• Discipline specific
• Search: http://service.re3data.org/browse/by-subject/
• Generic
• Figshare - https://figshare.com/
• Dryad - http://datadryad.org/
• CSU Digital Repository
• http://lib.colostate.edu/digital-collections/ http://67.media.tumblr.com/6228cbe58a9652f1a85e8a
b1ed08d715/tumblr_inline_n6oukhNlZW1qf11bs.png

56. Tool: CSU digital repository
• Over 100 Datasets
• Satisfy requirements
for manuscripts and
grants
• At no cost <1 TB
• $150/TB for 5 years
• $300/TB for >5 years

57. Theses and Dissertation Data
1. Submit to ProQuest with thesis or dissertation
• Supplemental data file
• Only discoverable through thesis or dissertation
2. Submit to CSU Library separately
• Requires distinctive descriptive metadata
• Linked with thesis or dissertation
• Data discoverable globally

58. Q’s: Access methods
Where will people be able to access the data?
Does your discipline have a repository?
Are you complying with CSU’s data policy?
How will you format the data for CSU digital repository?

59. Need help?
• General: library_data@colostate.edu
• Direct: tobin.magle@colostate.edu
• DMPTool: http://dmptool.org/
• Data Management Services website:
http://lib.colostate.edu/services/data-management